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Dataset Title:  8-day average satellite (Sentinel 3A and 3B) chlorophyll and suspended matter
concentrations for coastal British Columbia and southeast Alaska
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Institution:  Hakai Institute   (Dataset ID: sentinel_3A_POLYMER_8Day)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Files | Make a graph
 
Dimensions ? Start ? Stride ? Stop ?  Size ?    Spacing ?
 time (UTC) ?      296    4 days 17h 53m 54s (uneven)
  < slider >
 latitude (degrees_north) ?      4639    -0.002694934 (even)
  < slider >
 longitude (degrees_east) ?      6494    0.002694934 (even)
  < slider >
 
Grid Variables (which always also download all of the dimension variables) 
 num_obs ?
 num_passes ?
 logchl_mean ?
 logchl_sigma ?
 logchl_counts ?
 logchl_p50 ?
 SPM_mean (mg/m^3) ?
 SPM_sigma (mg/m^3) ?
 SPM_counts ?
 SPM_p50 (mg/m^3) ?
 chl_conc_mean (mg/m^3) ?
 chl_conc_sigma (mg/m^3) ?
 chl_conc_counts ?
 chl_conc_p50 (mg/m^3) ?

File type: (more information)

(Documentation / Bypass this form) ?
 
(Please be patient. It may take a while to get the data.)


 

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.593216e+9, 1.714176e+9;
    String axis "T";
    String ioos_category "Time";
    String long_name "Time";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 actual_range 47.00098814229249, 59.5000898311175;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
    Float64 valid_max 59.5000898311175;
    Float64 valid_min 47.00098814229249;
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 actual_range -139.00062881782247, -121.50242544017246;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
    Float64 valid_max -121.50242544017246;
    Float64 valid_min -139.00062881782247;
  }
  num_obs {
    Int32 _FillValue -1;
    String long_name "Num Obs";
  }
  num_passes {
    Int16 _FillValue -1;
    String long_name "Num Passes";
  }
  logchl_mean {
    Float32 _FillValue NaN;
    String long_name "Logchl Mean";
  }
  logchl_sigma {
    Float32 _FillValue NaN;
    String long_name "Logchl Sigma";
  }
  logchl_counts {
    Float32 _FillValue NaN;
    String long_name "Logchl Counts";
  }
  logchl_p50 {
    Float32 _FillValue NaN;
    String long_name "Logchl P50";
  }
  SPM_mean {
    Float32 _FillValue NaN;
    String cell_method "time: mean (interval: 1 day comments: previous 8 day from present inclusively)";
    Float32 colorBarMaximum 50.0;
    Float32 colorBarMinimum 0.02;
    String colorBarPalette "Spectrum";
    String colorBarScale "Log";
    String long_name "SPM Mean";
    String units "mg/m^3";
  }
  SPM_sigma {
    Float32 _FillValue NaN;
    String cell_method "time: standard deviation (interval: 1 day comments: previous 8 day from present inclusively)";
    Float32 colorBarMaximum 5.0;
    Float32 colorBarMinimum 0.005;
    String colorBarPalette "KT_thermal";
    String colorBarScale "Log";
    String long_name "SPM Sigma";
    String units "mg/m^3";
  }
  SPM_counts {
    Float32 _FillValue NaN;
    String cell_method "time: count (interval: 1 day comments: previous 8 day from present inclusively)";
    String long_name "SPM Counts";
  }
  SPM_p50 {
    Float32 _FillValue NaN;
    String cell_method "time: median (interval: 1 day comments: previous 8 day from present inclusively)";
    Float32 colorBarMaximum 50.0;
    Float32 colorBarMinimum 0.02;
    String colorBarPalette "Spectrum";
    String colorBarScale "Log";
    String long_name "SPM P50";
    String units "mg/m^3";
  }
  chl_conc_mean {
    Float32 _FillValue NaN;
    String cell_method "time: mean (interval: 1 day comments: previous 8 day from present inclusively)";
    Float32 colorBarMaximum 40.0;
    Float32 colorBarMinimum 0.15;
    String colorBarPalette "Spectrum";
    String colorBarScale "Log";
    String long_name "Chl Conc Mean";
    String standard_name "mass_concentration_of_chlorophyll_a_in_sea_water";
    String units "mg/m^3";
  }
  chl_conc_sigma {
    Float32 _FillValue NaN;
    String cell_method "time: standard deviation (interval: 1 day comments: previous 8 day from present inclusively)";
    Float32 colorBarMaximum 5.0;
    Float32 colorBarMinimum 0.005;
    String colorBarPalette "KT_thermal";
    String colorBarScale "Log";
    String long_name "Chl Conc Sigma";
    String standard_name "mass_concentration_of_chlorophyll_a_in_sea_water";
    String units "mg/m^3";
  }
  chl_conc_counts {
    Float32 _FillValue NaN;
    String cell_method "time: count (interval: 1 day comments: previous 8 day from present inclusively)";
    String long_name "Chl Conc Counts";
  }
  chl_conc_p50 {
    Float32 _FillValue NaN;
    String cell_method "time: median (interval: 1 day comments: previous 8 day from present inclusively)";
    Float32 colorBarMaximum 40.0;
    Float32 colorBarMinimum 0.15;
    String colorBarPalette "Spectrum";
    String colorBarScale "Log";
    String long_name "Chl Conc P50";
    String standard_name "mass_concentration_of_chlorophyll_a_in_sea_water";
    String units "mg/m^3";
  }
  NC_GLOBAL {
    String cdm_data_type "Grid";
    String citation "Costa, M., & Hakai Institute. (2023). Sentinel-3A OLCI Imagery - Automated daily POLYMER processed satellite chlorophyll concentrations for coastal British Columbia and southeast Alaska (Version v1). Hakai Institute";
    String contributor_name "Maycira Costa - Spectral Laboratory";
    String contributor_role "Principal Investigator - Collaborator - Originator";
    String Conventions "CF-1.6, COARDS, ACDD-1.3";
    String creator_institution "Hakai Institute";
    String creator_name "Hakai Institute";
    String creator_type "institution";
    String creator_url "https://www.hakai.org/";
    String date_created "2023-08-01";
    String defaultGraphQuery "chl_conc_mean[last][0][0:last][0:last]&.draw=surface&.vars=longitude|latitude|temp";
    Float64 Easternmost_Easting -121.50242544017246;
    Float64 geospatial_lat_max 59.5000898311175;
    Float64 geospatial_lat_min 47.00098814229249;
    Float64 geospatial_lat_resolution 0.0026949335249730503;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -121.50242544017246;
    Float64 geospatial_lon_min -139.00062881782247;
    Float64 geospatial_lon_resolution 0.0026949335249730503;
    String geospatial_lon_units "degrees_east";
    String history 
"2024-04-28T12:41:23Z (local files)
2024-04-28T12:41:23Z https://catalogue.hakai.org/erddap/griddap/sentinel_3A_POLYMER_8Day.das";
    String id "d1bef0b7-4d15-4bc1-bf34-faca6352891f";
    String infoUrl "???";
    String institution "Hakai Institute";
    String keywords "abundance and biomass, chlorophyll a, coastal zone, phytoplankton, remote sensing, water quality";
    String license "https://creativecommons.org/licenses/by/4.0";
    String metadata_profile "beam";
    String metadata_version "0.5";
    String naming_authority "ca.cioos";
    Float64 Northernmost_Northing 59.5000898311175;
    String platform "satellite";
    String platform_vocabulary "https://vocab.nerc.ac.uk/collection/L06/current/";
    String product_type "BINNED-L3";
    String product_version "1";
    String project "Oceanography";
    String publisher_institution "Hakai Institute";
    String publisher_name "Hakai Institute";
    String publisher_type "institution";
    String publisher_url "https://www.hakai.org/";
    String references "https://doi.org/";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 47.00098814229249;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary 
"This is an ongoing dataset of fully processed 8-day average Sentinel 3A and 3B chlorophyll-a (Chla) and suspended particulate matter (SPM) imagery for coastal and offshore British Columbia (BC) and Southeast Alaska waters. Sentinel 3A and 3B are European Space Agency (ESA) oceanography satellites jointly operated with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The ocean and land colour instrument (OLCI) onboard both satellites has a 300m spatial resolution, near daily temporal coverage (when 3A and 3B are combined), 21 spectral bands from 400-1200nm, high signal-to-noise ratio and an off-nadir swath centered to minimize ocean sun glint. These features make the instruments well suited to retrievals of biogeochemical products from optically complex coastal waters.

            At the University of Victoria (BC, Canada), the SPECTRAL remote sensing laboratory has performed extensive evaluation of methods for the best regional Chla and SPM retrievals. Validation with in-situ data showed the best results using Level-1 imagery processed with the POLYnomial based algorithm applied to MERIS (POLYMER) processor. Following validation, the SPECTRAL laboratory and the MOD(ularity) Squad developed an automated processing system that: 1) downloads imagery from the Marine Copernicus Online Data Access (CODA) web service; 2) applies POLYMER and flagging and; 3) mosaics the imagery for fully processed Chla and SPM concentrations over the study region. Additionally, an interactive public web interface was created to view the near real time outputs at www.algaeexplorer.ca (provided in resources).  Full validation details are provided in Giannini et al. (2021) and processing details in Jacoby et al. (2019) and Marchese et al. (2022) referenced in the resources.
            
            In 2022, the Hakai Institute took responsibility of the project, added processing of 3B imagery and 8-day averaging of 3A and 3B imagery, created automated submission to the Canadian Integrated Ocean Observing System (CIOOS) and updated the Algae Explorer web interface to include all products. Averaging imagery over 8 days considerably improves spatial coverage in cloudy regions such as the northeast Pacific.
            
            This product provides the best known regional OLCI Chla and SPM retrievals shown to have low systematic biases (<1%) and follow expected seasonal and spatial trends; however, relative percent difference between validation data and satellite retrievals was high notably for Chla (~83%) due to the underestimation of high Chla concentrations and potentially an artifact of spatial-temporal mismatches between validation samples and image pixels. The SPECTRAL laboratory has performed initial radiometric comparisons of 3A and 3B imagery and shown highly comparable data; however, comparison of biogeochemical outputs is still in progress. Further uncertainties exist in high turbidity regions (Fraser River plume and fjords) where uncorrectable poor-quality pixels are sometimes observed. Additionally, an unresolved data striping issue is periodically present and observed as a narrow band of distorted pixels, which sometimes evades the land mask and, crosses images diagonally above Vancouver Island. Efforts are underway to correct this issue. Data users should consider these uncertainties and issues when using the data. 
            
            Satellite remote sensing is increasingly used to study surface ocean processes at the spatial and temporal resolutions required for understanding long term variability under a changing climate. Chlorophyll-a is the most widely used measure of phytoplankton biomass and crucial for understanding phytoplankton which are the base of the marine food web and control ocean biogeochemical cycling. Suspended particulate matter is a key water quality indicator (i.e. turbidity) with increased concentrations reducing light availability to aquatic species. 
            
            Funding for validation and automation development was provided by the UBC/UVic Hakai Coastal Initiative postdoctoral fellowship, NSERC NCE Marine Environmental Observation Prediction and Response (MEOPAR) network, Canadian Space Agency (CSA), Canadian Foundation for Innovation (CFI) and NSERC Discover Grant awarded to Maycira Costa. 
            
            It is requested that Giannini et al. (2021), Jacoby et al. (2019) and Marchese et al. (2022) are referenced if data is used for published research and the ESA acknowledged as the data provider.";
    String TileSize "64:64";
    String time_coverage_end "2024-04-27T00:00:00Z";
    String time_coverage_start "2020-06-27T00:00:00Z";
    String title "8-day average satellite (Sentinel 3A and 3B) chlorophyll and suspended matter concentrations for coastal British Columbia and southeast Alaska";
    Float64 Westernmost_Easting -139.00062881782247;
  }
}

 

Using griddap to Request Data and Graphs from Gridded Datasets

griddap lets you request a data subset, graph, or map from a gridded dataset (for example, sea surface temperature data from a satellite), via a specially formed URL. griddap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its projection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

griddap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/griddap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.htmlTable?analysed_sst[(2002-06-01T09:00:00Z)][(-89.99):1000:(89.99)][(-179.99):1000:(180.0)]
Thus, the query is often a data variable name (e.g., analysed_sst), followed by [(start):stride:(stop)] (or a shorter variation of that) for each of the variable's dimensions (for example, [time][latitude][longitude]).

For details, see the griddap Documentation.


 
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